Visually Blog » Jon Schwabishhttp://blog.visual.ly
Mon, 02 Mar 2015 18:28:39 +0000en-UShourly1http://wordpress.org/?v=3.9.2The Graphic Continuumhttp://blog.visual.ly/graphic-continuum/
http://blog.visual.ly/graphic-continuum/#commentsWed, 01 Oct 2014 13:00:51 +0000http://blog.visual.ly/?p=21473Jon Schwabish and Severino Ribecca recently released a poster taxonomy of different types of charts, and how they all relate to each other. We think this is a great resource for designers everywhere, so we were especially interested in their take on the project. The Graphic Continuum began as I thought about the different ways we can plot data into different types of charts. My understanding of the different relationships between charts evolved over time by reading a variety of data visualization books, sketching different ideas and layouts, and presenting my ideas to different audiences. As my thoughts developed, I wanted to create a visualization that showed these different chart types and how they relate to one another, and I wanted to create something tangible that people could hold and point to as they worked with their data and their visualizations. I eventually teamed up with Severino Ribecca—who created and... keep reading

]]>Jon Schwabish and Severino Ribecca recently released a poster taxonomy of different types of charts, and how they all relate to each other. We think this is a great resource for designers everywhere, so we were especially interested in their take on the project.

The Graphic Continuum began as I thought about the different ways we can plot data into different types of charts. My understanding of the different relationships between charts evolved over time by reading a variety of data visualization books, sketching different ideas and layouts, and presenting my ideas to different audiences. As my thoughts developed, I wanted to create a visualization that showed these different chart types and how they relate to one another, and I wanted to create something tangible that people could hold and point to as they worked with their data and their visualizations. I eventually teamed up with Severino Ribecca—who created and runs The Data Visualisation Catalogue—to help me with the design.

Simply put, one of the biggest challenges of visualizing chart types is that there are just a lot of ways to visualize data. Take a column chart, for example, and bend it into a circle and you have a donut chart. Fill that in and you have a pie chart. Blow it up in all different directions—a nightingale.

But how do you create a visualization of an inherently nonlinear, complex system of graphic types? Others have tried to create a classification system for graphic types (here, here, and maybe even here),but, and perhaps by necessity, each simplifies the number of graph and data types.

The first drafts of The Graphic Continuum were laid out in a grid with a single dot in the top-left. But this layout lacked direction and a story and instead was a clutter of graphs; it wasn’t clear that people should start in the top-left graph and then make their way—some way, any way—through it. It was just a cluttered collection of graphs.

In an attempt to better organize the space, we started dividing graphs into different categories: Comparing Categories, Distribution, Geospatial, Part-to-Whole, Relationships, and Time. But this was no simple feat as there are functional overlaps between many chart types; for example, you can plot time series data as a line chart or as a column chart. We collected as many examples as we could and drew on a variety of resources to categorize the graphs based on what seemed to be the primary function of the chart. For example, time series data are primarily visualized using line charts and data that compare categories are primarily visualized using column charts.

We then had an organizing principle in mind, but we were far from where we needed to be. A few other edits, layouts, and approaches didn’t get us any further: we added then subtracted a side bar that showed basic visual encodings; we tried coloring the groups based on these encodings instead of our five groups; we added then subtracted links within and across groups; we rotated the space vertically; and we played around with different colors, fonts, and title bars.

Feeling stuck, we went analog. I hand-colored and cut out each graph in the original, grid version. Playing around in this way, I tried a circle layout, again relying on the basic five groups. We tried this in the electronic version, but it didn’t feel like a great use of the space. We felt we could do better than the circle layout.

After more toying around (and with helpful advice from some friends), we came to the current version. Using our thought process from the original format, we grouped the different graphs into our five categories and—this was crucial—we separated them across the layout. We could now add linking annotations to show connections between chart types. We went back and forth several times to figure out how many and which links to include, and what they should look like; we— then cut some graphs and text, added in some more, and iterated back and forth.

We believe The Graphic Continuum is a more comprehensive view of graphic types and how they can be classified into different categories. We hope you can use it as a tool to help decide how to choose the best graph for your data or to expose people to less common graphic types. Or, perhaps you can just use it as a piece of art in your home or office. Either way, we hope you find it useful and beautiful.

]]>http://blog.visual.ly/graphic-continuum/feed/0Good Presentation Technique: Layeringhttp://blog.visual.ly/good-presentation-technique-layering/
http://blog.visual.ly/good-presentation-technique-layering/#commentsMon, 29 Jul 2013 13:00:46 +0000http://blog.visual.ly/?p=14151Far too many of the hundreds of presentations I’ve seen in economics and public policy have relied too much on text and bullet lists. Why? It’s probably because text and bullets also serve the function of the old-fashioned pack of 3×5 cards: They help the presenter stick to the story. (Practicing is a better idea, but that’s a topic for another day.) Lots of experts (including some of my favorites, Nancy Duarte, Carmine Gallo, and Garr Reynolds) advocate using images. Visuals, they argue, are more likely to be remembered than slides that are dense with text, tables, or data. Make that point, however, and prepare for two main objections: How am I going to use a stunning picture — a sunset? a flower? an animal? — to support my discussion of health care costs or the unemployment rate? If I don’t put up a lot of text and data, how... keep reading

]]>Far too many of the hundreds of presentations I’ve seen in economics and public policy have relied too much on text and bullet lists. Why? It’s probably because text and bullets also serve the function of the old-fashioned pack of 3×5 cards: They help the presenter stick to the story. (Practicing is a better idea, but that’s a topic for another day.)

Lots of experts (including some of my favorites, Nancy Duarte, Carmine Gallo, and Garr Reynolds) advocate using images. Visuals, they argue, are more likely to be remembered than slides that are dense with text, tables, or data.

Make that point, however, and prepare for two main objections:

How am I going to use a stunning picture — a sunset? a flower? an animal? — to support my discussion of health care costs or the unemployment rate?

If I don’t put up a lot of text and data, how will the audience know I’m smart?

I’m coming to believe that effective presentations require a more deliberate approach than simply swapping out all of the dense text for images. Instead, I’m coming to appreciate a technique I call layering. (Other commentators, like Stephanie Evergreen, call this the slow reveal; Edward Tufte, with characteristic derision, calls it the dreaded build.) When they use layering, presenters still show the audience all of the ideas, just one step at a time.

Thus, instead of this:

How about this?

Layering lets the presenter show everything they want, but it helps focus the viewer’s attention where they want it.

I can make a similar case for technical graphics. Instead of this:

How about this? (Note the Twitter-like heads, as urged by Carmine Gallo.)

It’s not going to be easy to completely remake the technical and scientific presentation culture, but better slides will give audiences a reason to stay engaged and perhaps even to act on what they learn.

]]>http://blog.visual.ly/good-presentation-technique-layering/feed/0Can Government Learn from Data Visualization?http://blog.visual.ly/can-government-learn-from-data-visualization/
http://blog.visual.ly/can-government-learn-from-data-visualization/#commentsWed, 19 Dec 2012 18:00:38 +0000http://blog.visual.ly/?p=8282Can better data visualization bolster government communications with the public, the press, and policymakers? The answer is “Yes,” but to ensure the results, government agencies need to improve both the way they use graphical displays in their written presentations and how their analysts present their work verbally to an audience. If communications of data, findings, and analysis improve, perhaps everyone’s understanding of the nation’s public policy challenges will improve as well, and the result will be not just better government but better governance. There is no shortage of graphics from government agencies that fail on even basic good Data Visualization practices. As an example, take this recent graphic from the General Accountability Office (GAO): At first blush — and at second and third — it is very hard to tell what’s going on. The pie chart on the left side (the perennial thorn in the side of data visualization) is... keep reading

]]>Can better data visualization bolster government communications with the public, the press, and policymakers? The answer is “Yes,” but to ensure the results, government agencies need to improve both the way they use graphical displays in their written presentations and how their analysts present their work verbally to an audience. If communications of data, findings, and analysis improve, perhaps everyone’s understanding of the nation’s public policy challenges will improve as well, and the result will be not just better government but better governance.

There is no shortage of graphics from government agencies that fail on even basic good Data Visualization practices. As an example, take this recent graphic from the General Accountability Office (GAO):

At first blush — and at second and third — it is very hard to tell what’s going on. The pie chart on the left side (the perennial thorn in the side of data visualization) is somehow decomposed into the stacked column chart on the right side with the distribution on the left converted to a different distribution on the right. What about two separate stacked column charts? Or how about a slopegraph connecting the two? There are any number of different ways these data could be presented, but this one perhaps does the best job of not telling a story clearly.

This graphic is from the U.S. Department of Agriculture:

If you can ignore the 3D column chart—which distorts the data in such a way that the $1,800 value for the “Certification” category does not even reach the gridline for $1,800—you can try to figure out what is meant by the “MILLIONS/BILLIONS” label on the y-axis.

To begin, government analysts should use the most basic insights and strategies from the data visualization community. Here are 5 beginning steps that analysts can take to improve their graphics:

Deemphasize gridlines, tick marks, and data labels.

Keep labels and data close together.

Avoid pie charts and 3D charts (and never use 3D pie charts).

Consider fonts and colors carefully.

And most importantly, Show the Data (because that’s why people are reading the analysis).

Another way government can do a better job communicating is to improve the presentation slides that analysts use when they speak to an audience. In her many books and tutorials, Nancy Duarte emphasizes connecting with the audience by starting a presentation with a story. And many presentation experts suggest minimizing the amount of text and maximizing the number of images. Unfortunately, not many government analysts and researchers are familiar with these tools and thus many slides are packed with dense text, bullet points, and mathematical equations. For example, take this recent testimony before select members of the House of Representatives:

What I take away from this presentation is that I’m not going to hear a word the speaker says because I’m too busy reading the slides!

Because people in the data visualization community are adept at presenting data, their verbal presentations also tend to be well-designed. Here are 5 beginning steps that analysts can take to improve their presentations*:

Tell an interesting story, and start your presentation with a theme to help your audience connect.

Drop the bullet points—illustrate concepts instead.

Don’t give a document, give a presentation; don’t pack every piece of information into your slides.

Design a cohesive color scheme and layout for your presentation.

Be well prepared.

(*Derived from works by Nancy Duarte, Carmine Gallo, Jesse Desjardins, and The Presentation Designer)

In the end, I think government analysts have a lot to learn from the data visualization community and as static infographics, dynamic interactive data tools, and other data visualization techniques and products become more popular and easier to use, government analysts will hopefully catch on and improve their graphics and presentations as well.